scholarly journals Inference and Influence of Large-Scale Social Networks Using Snapshot Population Behaviour without Network Data

2020 ◽  
Author(s):  
Antonia Godoy-Lorite ◽  
Nick S. Jones
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Large Scale ◽  
Social Preferences ◽  
Mayoral Elections ◽  
Large Scale Network ◽  
Scale Network ◽  
Eu Referendum ◽  
Behaviour Type ◽  
The Eu

Population behaviours, such as voting and vaccination, depend on social networks. Social networks can differ depending on behaviour type and are typically hidden. However, we do often have large-scale behavioural data, albeit only snapshots taken at one timepoint. We present a method that jointly infers large-scale network structure and a networked model of human behaviour using only snapshot population behavioural data. This exploits the simplicity of a few-parameter, geometric socio-demographic network model and a spin-based model of behaviour. We illustrate, for the EU Referendum and two London Mayoral elections, how the model offers both prediction and the interpretation of our homophilic inclinations. Beyond offering the extraction of behaviour-specific network-structure from large-scale behavioural datasets, our approach yields a crude calculus linking inequalities and social preferences to behavioural outcomes. We give examples of potential network-sensitive policies: how changes to income inequality, a social temperature and homophilic preferences might have reduced polarisation in a recent election.

scholarly journals Truss Decomposition using Triangle Graphs

2021 ◽  
Author(s):  
Mohsen Rezvani ◽  
Mojtaba Rezvani
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
State Of The Art ◽  
Decomposition Algorithms ◽  
Scalable Algorithm ◽  
Large Scale Network ◽  
Triangle Graph ◽  
Speed Up ◽  
Scale Network ◽  
High Degree

Abstract Recent studies have shown that social networks exhibit interesting characteristics such as community structures, i.e., vertexes can be clustered into communities that are densely connected together and loosely connected to other vertices. In order to identify communities, several definitions have been proposed that can characterize the density of connections among vertices in the networks. Dense triangle cores, also known as $k$-trusses, are subgraphs in which every edge participates at least $k-2$ triangles (a clique of size 3), exhibiting a high degree of cohesiveness among vertices. There are a number of research works that propose $k$-truss decomposition algorithms. However, existing in-memory algorithms for computing $k$-truss are inefficient for handling today’s massive networks. In this paper, we propose an efficient, yet scalable algorithm for finding $k$-trusses in a large-scale network. To this end, we propose a new structure, called triangle graph to speed up the process of finding the $k$-trusses and prove the correctness and efficiency of our method. We also evaluate the performance of the proposed algorithms through extensive experiments using real-world networks. The results of comprehensive experiments show that the proposed algorithms outperform the state-of-the-art methods by several orders of magnitudes in running time.

scholarly journals LogicSNN: A Unified Spiking Neural Networks Logical Operation Paradigm

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2123 ◽  
Author(s):  
Lingfei Mo ◽  
Minghao Wang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Network Structure ◽  
Large Scale ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Logical Operation ◽  
Large Scale Network ◽  
Learning Capabilities ◽  
Scale Network

LogicSNN, a unified spiking neural networks (SNN) logical operation paradigm is proposed in this paper. First, we define the logical variables under the semantics of SNN. Then, we design the network structure of this paradigm and use spike-timing-dependent plasticity for training. According to this paradigm, six kinds of basic SNN binary logical operation modules and three kinds of combined logical networks based on these basic modules are implemented. Through these experiments, the rationality, cascading characteristics and the potential of building large-scale network of this paradigm are verified. This study fills in the blanks of the logical operation of SNN and provides a possible way to realize more complex machine learning capabilities.

Someone To Talk To

2017 ◽  
Author(s):  
Mario Luis Small
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Graduate Students ◽  
Network Analysis ◽  
Large Scale ◽  
First Year ◽  
Weak Ties ◽  
Strong Ties ◽  
Large Scale Network ◽  
Scale Network

When people are facing difficulties, they often feel the need for a confidant—a person to vent to or talk things through with who will offer sympathy or understanding. How do they decide on whom to rely? In theory, the answer seems obvious: if the matter is personal, they will turn to a spouse, a family member, or someone otherwise close. In practice, what people actually do often belies these expectations. This book follows a group of graduate students as they cope with the stress of their first year in their programs, probing how they choose confidants over the course of their everyday experiences and unraveling the implications of the process. The book then tests its explanations against data on national populations. It shows that rather than consistently rely on their “strong ties,” people often take pains to avoid close friends and family, because these are too fraught with complex expectations. People often confide in “weak ties,” as their fear that their trust could be misplaced is overcome by their need for one who understands. In fact, people may find themselves confiding in acquaintances and even strangers unexpectedly, without much reflection on the consequences. Amid a growing wave of big data and large-scale network analysis, the book returns to the basic questions of who we connect with, how, and why, and upends decades of conventional wisdom on how we should think about and analyze social networks.

scholarly journals Targeted Influential Nodes Selection in Location-Aware Social Networks

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Susu Yang ◽  
Hui Li ◽  
Zhongyuan Jiang
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Seed Quality ◽  
Approximate Algorithm ◽  
Maximization Problem ◽  
Target Area ◽  
Large Scale Network ◽  
Location Aware ◽  
Scale Network

Given a target area and a location-aware social network, the location-aware influence maximization problem aims to find a set of seed users such that the information spread from these users will reach the most users within the target area. We show that the problem is NP-hard and present an approximate algorithm framework, namely, TarIM-SF, which leverages on a popular sampling method as well as spatial filtering model working on arbitrary polygons. Besides, for the large-scale network we also present a coarsening strategy to further improve the efficiency. We theoretically show that our approximate algorithm can provide a guarantee on the seed quality. Experimental study over three real-world social networks verified the seed quality of our framework, and the coarsening-based algorithm can provide superior efficiency.

scholarly journals Small- and large-scale network structure of live fish movements in Scotland

2009 ◽  
Vol 91 (2-4) ◽  
pp. 261-269 ◽  
Author(s):  
Darren Michael Green ◽  
Alison Gregory ◽  
Lorna Ann Munro
Keyword(s):  
Network Structure ◽  
Large Scale ◽  
Fish Movements ◽  
Large Scale Network ◽  
Live Fish ◽  
Scale Network

Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Large Scale Network ◽  
Brand Advertising ◽  
Scale Network
Sign in / Sign up

Export Citation Format

Share Document